Atlas for automatic segmentation of retina layers from OCT images

Abstract

A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

What is claimed is: 
     
       1. A method for segmenting a medical image comprising:
 receiving a volumetric medical image comprising a plurality of slices, each slice being adjacent to at least one other slice in the image; 
 selecting an initial slice; 
 segmenting the initial slice based at least in part on a constructed shape model; 
 applying a label to each segmented layer in the initial slice; and 
 segmenting at least one slice adjacent to the initial slice based at least in part on the segmented initial slice 
 wherein segmenting the at least one slice adjacent to the initial slice based at least in part on the segmented initial slice includes, for a pixel in the at least one slice, transforming the pixel to the initial slice, initializing a window, searching within the window for pixels with a corresponding value in the initial slice, and calculating a shape prior probability based on the labels of found pixels with corresponding values, and labeling the pixel in the at least one slice based on the shape prior probability. 
 
     
     
       2. The method of  claim 1 , wherein the medical images are retinal images. 
     
     
       3. The method of  claim 1 , wherein the medical images are optical coherence tomography images. 
     
     
       4. The method of  claim 1 , wherein the initial slice has two adjacent slices. 
     
     
       5. The method of  claim 1 , wherein the volumetric medical image is a 3-D medical image and wherein the plurality of slices are a plurality of 2-D medical images. 
     
     
       6. The method of  claim 1 , wherein segmenting the initial slice includes aligning the initial slice to the constructed shape model. 
     
     
       7. The method of  claim 6 , wherein segmenting the initial slice further includes applying a joint model to the initial slice subsequent to alignment. 
     
     
       8. The method of  claim 1 , wherein segmenting the at least one slice adjacent to the initial slice includes aligning the at least one slice to the initial slice. 
     
     
       9. The method of  claim 1 , wherein each segmented layer corresponds to a retinal layer. 
     
     
       10. The method of  claim 1 , wherein the pixel in the at least one slice has a reflectivity value and wherein searching within the window for pixels with the corresponding value in the initial slice comprises searching within the window for pixels with corresponding reflectivity values in the initial slice. 
     
     
       11. A method for segmenting a 3-D medical image comprising:
 receiving a 3-D medical image, the 3-D medical image comprising an array of adjacent 2-D medical images; 
 segmenting an initial 2-D medical image based at least in part on a constructed shape model; and 
 segmenting a 2-D medical image adjacent to the segmented initial 2-D medical image based at least in part on the segmented initial 2-D medical image 
 wherein segmenting the 2-D medical image based at least in part on the previously segmented 2-D medical image includes, for a pixel in the 2-D medical image, determining a value for the pixel, transforming the pixel to the previously segmented 2-D medical image, initializing a window, searching within the window for pixels with a corresponding value in the previously segmented 2-D medical image, calculating a shape prior probability based on labels of found pixels with corresponding values, and labeling the pixel in the 2-D medical image based on the shape prior probability. 
 
     
     
       12. The method of  claim 11 , further comprising, after the step of segmenting the 2-D medical image, repeating the step of segmenting the 2-D medical image until all 2-D medical images in the array are segmented. 
     
     
       13. The method of  claim 11 , wherein the 3-D medical image is a retinal image depicting at least a fovea, and wherein the initial 2-D medical image extends through the fovea. 
     
     
       14. The method of  claim 11 , wherein segmenting the initial 2-D medical image includes aligning the initial 2-D medical image to the constructed shape model and applying a joint model to the initial 2-D medical image subsequent to alignment. 
     
     
       15. The method of  claim 11 , wherein the initial 2-D medical image depicts an anatomical feature and wherein the constructed shape model is constructed from a database of images of the anatomical feature. 
     
     
       16. The method of  claim 15 , wherein the anatomical feature is a fovea. 
     
     
       17. The method of  claim 11 , wherein the value for the pixel is a reflectivity value for the pixel.